Papers
6
Total Citations
39
H-Index
4
About
Yunfeng Ji is a robotics researcher whose work spans intelligent robot control, motion planning, and autonomous systems. His research is most prominently centered on table tennis robotics, where he has made notable strides in addressing real-world challenges that earlier systems overlooked. His 2021 paper on model-based trajectory prediction and hitting velocity control — his most cited work with 19 citations — introduced a novel framework that moved beyond canonical position control to incorporate realistic velocity requirements, reflecting a more practical approach to robot-human interaction. Building on this, his 2023 work extended the framework further by integrating opponent behavior prediction to improve hitting accuracy, demonstrating a consistent drive to close the gap between laboratory robots and competitive, adaptive systems. Beyond table tennis, Ji has contributed meaningfully to mobile robotics and bioinspired systems. His research on snake robot collision avoidance using MPC-based optimization and his work on improved artificial potential field methods for mobile robot path planning in health monitoring applications highlight his breadth across planning, control, and real-world deployment. More recently, his exploration of curriculum reinforcement learning for catching spinning balls signals a growing interest in learning-based approaches. With a cumulative citation record reflecting steady recognition, Ji is an emerging contributor shaping the future of intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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